An increasing number of distributed applications process continuously flowing data from geographically distributed sources, perform analytics on the streamed data, and provide analysis results to entities that may also be geographically distributed. The continuously flowing data may be generated from sensor measurements that capture real-time data describing current operating characteristics of a remote device. The sensor measurements may derive from multiple different types of sensors installed at various locations (e.g., brakes, engine, steering mechanism, cooling system, passenger ventilation, power source, etc.) on a currently-moving vehicle, aircraft or watercraft, for example.
Event stream processing (ESP) can be used to analyze and understand millions of events per second, while detecting patterns of interest as they occur in real time. While processing with sub-millisecond response times for high-volume throughput, data streams can be assessed with ESP to derive insights and take appropriate actions.
ESP models are developed to perform the streaming analytics on real-time data streams, for example, as part of the Internet of Things (IoT). The ESP models may clean, transform, aggregate, calculate new variable values from, analyze, predict new variable values from, output, generate an alert on, etc. the received streamed data. Existing ESP analytics platforms merely provide the architecture to support ESP model generation to solve various analytics problems. As a result, analysts and software developers must design and develop the source code to implement the ESP model to solve each analytic problem, which requires significant development and testing time before implementation of the ESP model to solve real-world problems.